P2257Self-care abilities and depression symptoms linked to brain injury in heart failure
Bibliographic record
Abstract
Abstract Heart failure (HF) patients show brain damage in regions controlling mood, autonomic, and cognitive functions. HF patient's exhibit symptoms related to depression, anxiety, poor sleep quality, daytime sleepiness, impaired cognition, and poor self-care, however, the relationship between brain damage and disease related symptoms is unclear. Therefore, our aim was to correlate gray matter density in prefrontal cortices, hippocampus, and insular lobe with disease symptoms using T1-weight voxel based morphometry procedure and evaluate the measure that predicts the brain damage. Methods Two high-resolution T1-weighted data were collected from each 42 HF subjects (age, 55.65±7.61; 30 male; LVEF 27.74±9.40%; NYHA class II/III) using a 3.0-Tesla MRI scanner. Depression, anxiety, sleep quality, daytime sleepiness, cognition, and self-care issues were examined with Beck Depression Inventory (BDI), Beck Anxiety Inventory (BAI), Pittsburgh Sleep Quality Index (PSQI), Epworth Sleepiness Scale (ESS), Montreal Cognitive Assessment (MoCA), and Self-Care of Heart Failure Inventory (SCHFI) respectively. Both T1-weighted scans were realigned, averaged, and segmented into gray matter (GM), white matter, and cerebrospinal fluid tissue type. GM maps were normalized (unmodulated) to a common space and smoothed to obtain GM density maps. The smoothed GM density maps were used to examine the association of brain damage with disease symptoms (SPM 12; partial correlation; covariates, age and gender; p<0.005). Region of interest analyses were performed in prefrontal cortices, insular lobe and hippocampus to evaluate the correlation using partial correlation (SPSS, covariates, age and gender, p<0.05). Results The average scores for BAI 9.55±9.90, BDI 8.69±7.05, ESS 7.00±3.72, MoCA 24.64±3.48, PSQI 6.62±3.90, maintenance 73.12±13.78, management 70.28±13.98, and confidence 73.77±15.96 were calculated. Significant negative correlations was observed between GM density values and BAI (prefrontal cortices, r=−0.532, p<0.001), BDI (prefrontal cortices, r=−0.60, p<0.001; insula, r=−0.528, p<0.001, hippocampus, r=−0.432, p<0.005), ESS (prefrontal cortices, r=−0.419, p=0.007; insula, r=−0.443, p=0.004), and PSQI (prefrontal cortices, r=−0.517, p=0.001) scores, and positive correlation between GM density values, MoCA (prefrontal cortices, r=0.462, p=0.003) and SCHFI scores (maintenance: prefrontal cortices, r=0.422, p=0.007; insula, r=0.412, p=0.009, hippocampus, r=0.455, p=0.004; management: prefrontal cortices, r=0.728, p=0.001; insula, r=0.707, p=0.001, hippocampus, r=0.775, p<0.001) (Figure 1). Figure 1 Conclusions HF subjects show more wide-spread and significant correlations between regional GM density values and BDI as well as SCHFI management scores compared to the other disease symptom measures. The findings suggest that damage in mood, autonomic, and cognitive functions sites better correlate with BDI and SCHFI over other measures. Acknowledgement/Funding NIH/NINR 1R01 NR014669
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".